Properties can be accessed with getLearnerProperties(learner)
, which returns a
character vector.
The learner properties are defined as follows:
- numerics, factors, ordered
Can numeric, factor or ordered factor features be handled?
- functionals
Can an arbitrary number of functional features be handled?
- single.functional
Can exactly one functional feature be handled?
- missings
Can missing values in features be handled?
- weights
Can observations be weighted during fitting?
- oneclas, twoclass, multiclass
Only for classif: Can one-class, two-class or multi-class classification problems be handled?
- class.weights
Only for classif: Can class weights be handled?
- rcens, lcens, icens
Only for surv: Can right, left, or interval censored data be handled?
- prob
For classif, cluster, multilabel, surv: Can probabilites be predicted?
- se
Only for regr: Can standard errors be predicted?
- oobpreds
Only for classif, regr and surv: Can out of bag predictions be extracted from the trained model?
- featimp
For classif, regr, surv: Does the model support extracting information on feature importance?
Arguments
- learner
(Learner |
character(1)
)
The learner. If you pass a string the learner will be created via makeLearner.- props
(character)
Vector of properties to query.
Value
getLearnerProperties
returns a character vector with learner properties.
hasLearnerProperties
returns a logical vector of the same length as props
.
See also
Other learner:
getClassWeightParam()
,
getHyperPars()
,
getLearnerId()
,
getLearnerNote()
,
getLearnerPackages()
,
getLearnerParVals()
,
getLearnerParamSet()
,
getLearnerPredictType()
,
getLearnerShortName()
,
getLearnerType()
,
getParamSet()
,
helpLearnerParam()
,
helpLearner()
,
makeLearners()
,
makeLearner()
,
removeHyperPars()
,
setHyperPars()
,
setId()
,
setLearnerId()
,
setPredictThreshold()
,
setPredictType()